The world’s push toward an environmentally sustainable society is highly dependent on the semiconductor industry, due to carbon footprints of global-scale sources such as computing systems for virtual and extended reality applications (VR and XR). Despite previous carbon modeling efforts for such computing systems, there lacks a wide range of design tools to optimize total life cycle carbon footprint (during manufacturing and also during day-to-day operation), while meeting application-level constraints (power, performance, area). To address this need, we have developed a carbon-aware design framework that optimizes carbon efficiency of computing systems—quantified by metrics such as total Carbon Delay Product (tCDP: the product of total carbon and total application execution time)—while also identifying key design parameters for improving carbon efficiency. As a case study, we demonstrate the effectiveness of our framework to improve tCDP of hardware accelerators for artificial intelligence (AI) and XR applications. We show: (1) optimizing for carbon efficiency (tCDP) instead of energy efficiency (Energy-Delay Product or EDP) improves carbon efficiency by up to 6.9x—i.e., optimizing for EDP is insufficient; (2) for multi-core CPUs inside production VR headsets, optimizing number of cores (from 8 to 4) improves tCDP by 1.25x (over their entire lifetime); (3) leveraging an advanced three-dimensional integration (3D) technique (3D stacking of separately-fabricated logic and memory chips) can improve tCDP by 6.9x vs. conventional systems (no 3D stacking).

Given the performance and efficiency optimizations realized by the computer systems and architecture community over the last decades, the dominating source of computing’s carbon footprint is shifting from operational emissions to embodied emissions. These embodied emissions owe to hardware manufacturing and infrastructure-related activities. Despite the rising embodied emissions, there is a distinct lack of architectural modeling tools to quantify and optimize the end-to-end carbon footprint of computing. This work proposes ACT, an architectural carbon footprint modeling framework, to enable carbon characterization and sustainability-driven early design space exploration. Using ACT we demonstrate optimizing hardware for carbon yields distinct solutions compared to optimizing for performance and efficiency. We construct use cases, based on the three tenets of sustainable design—Reduce, Reuse, Recycle—to highlight future methods that enable strong performance and efficiency scaling in an environmentally sustainable manner.

Polymer matrix composites exhibit nonlinear viscoelastic behavior over a wide range of temperatures and loading frequencies, which requires an elaborate experimental characterization campaign. Methods are now available to accelerate the characterization process and recover elastic modulus from storage modulus (E′). However, these methods are limited to the linear viscoelastic region and need to be expanded to nonlinear viscoelastic problems to enable materials design. The present work aims to build a general machine learning based architecture to accelerate the characterization and materials design process for nonlinear viscoelastic materials using the E′ results. To expand outside the linear viscoelastic region, general relations of viscoelasticity are first developed so the master relation of E′ considering nonlinear viscoelasticity can be transformed to time domain relaxation function. The transform starts with building the master relation by optimizing the artificial neural network (ANN) formulation using Kriging model and genetic algorithm. Then the master relation is transformed to a relaxation function, which can be used to predict the stress response with a given strain history and to further extract the elastic modulus. The transform is tested on high density polyethylene matrix syntactic foams and the accuracy is found by comparing the predicted materials properties with those obtained from tensile tests. The good agreements indicate the transform can predict the elastic modulus under a wide range of temperatures and strain rates for any composition of the composite and can be used for material design problems.